Big Data: Debunking the Myths – Part 2
In a previous blog post, I defined Big Data and debunked some common myths surrounding it. (You can read the article here: Big Data: Debunking the Myths). To continue along this path, here are 5 more myths about Big Data I’d like to debunk:
5. Relational databases (and the exiting BI tools) can handle Big Data
I’ve met many people that use the term Big Data in place of very large data sets. They think that a relational database management system (RDBMS) can handle very large data sets. Volume is only one component of Big Data. There are other components such as verity and velocity.1
The key takeaway is this. RDBMS can handle only structured data; it can’t process Big Data. That requires specialized tools such as the SAP HANA platform and Apache Hadoop with in-memory computing power.
6. Big Data is too difficult to implement
Well, this is the one is tricky. Big Data is not difficult to implement, but what you’re trying to achieve through the implementation of Big Data can be difficult to realize. I introduced five technical Vs in part one of this blog series: verity, volume, velocity, viscosity, and virality. This time I’ll introduce two business-related “Vs” that are important for the Big Data implementations: Vision and Value. I like to think of it as “vision to value.” Once the use case or vision is set, then it’s easier to work towards realizing the value.
The key takeaway is that lots of Big Data projects fail because they lack a well-defined vision. If you establish the appropriate vision, you can progressively elaborate on that vision throughout the iterative implementation to achieve a positive outcome.
7. Big Data is for big companies
Large companies may have more internal sources of data, but even small firms can take advantage of data coming in from social media platforms, publicly available data (such as PUC- Public Utility Commission data), data from public agencies (such as WHO, UNICEF, etc.), and data vendors (such as Lexus Nexis, D&B or Nielsen). Small to midsize organizations can make data-driven decisions and make course corrections faster than their larger peers. They can also learn from the others’ implementation experiences and adopt industry specific best practices more quickly than their large-enterprise counterparts.2
The key takeaway is that the size of the organization does not matter to implement Big Data.
8. End users don’t need direct access to Big Data
With Big Data moving in at a high speed, from a wide variety of sources, and in large volumes, it might seem that it is just too complicated for end users to access and use. While data scientists typically do the heavy lifting during a Big Data implementation, it’s very important to involve your business users, industry experts, and other functional experts. “Vision to value” cannot be achieved without the participation of your end users.
For example, if you are implementing Big Data processes for gene sequencing, the data scientists can put together the solution for you. But the key end users (doctors and research scholars) are the ones who actually consume the data.
The key takeaway is that gaining participation from various levels in the organization – from executive sponsors to end users – is a critical success factor for a Big Data implementation.
9. The Big Data bubble will eventually burst
Some innovations may quickly inflate and then explode, like the dot.com bubble, but transformative technological changes such as BigData and Cloud will stick around. The dot-com crash did not signal the end of the Internet, just inflated business models. Big Data is new kid on the block, along with cloud and mobile technology, so everybody is talking about these topics.
Even after the hype settles, organizations will still have to address Big Data. Since the sources of data are so diverse, what is being generated is not strictly structured data anymore. Companies will have more Big Data to deal with than they ever expected, due to exponential growth.3 But fortunately, there are tools to collect structured, semi-structured, quasi-structured, and unstructured data.
The key takeaway is this: while the term Big Data might change, the reality of Big Data is here to stay.
Other references and further reading:
Check out the Nine Big Data Myths and Realities, and learn how SAP Services can help you define the right strategy to build a Big Data infrastructure to support your business goals: go here. Follow @SAPServices on Twitter and join the conversation: hashtags: #sap #services #bigdata